Improving resource utilisation in SLE drug development through innovative trial design
Bibliographic record
Abstract
SLE is a complex autoimmune disease with considerable unmet need. Numerous clinical trials designed to investigate novel therapies are actively enrolling patients straining limited resources and creating inefficiencies that increase enrolment challenges. This has motivated investigators developing novel drugs and treatment strategies to consider innovative trial designs that aim to improve the efficiency of generating evidence; these strategies propose conducting fewer trials, involving smaller numbers of patients, while maintaining scientific rigour in safety and efficacy data collection and analysis. In this review we present the design of two innovative phase IIb studies investigating efavaleukin alfa and rozibafusp alfa for the treatment of SLE which use an adaptive study design. This design was selected as a case study, investigating efavaleukin alfa, in the Food and Drug Administration's Complex Innovative Trial Design Pilot Program. The adaptive design approach includes prospectively planned modifications at predefined interim timepoints. Interim assessments of futility allow for a trial to end early when the investigational therapy is unlikely to provide meaningful treatment benefits to patients, which can release eligible patients to participate in other-potentially more promising-trials, or seek alternative treatments. Response-adaptive randomisation allows randomisation ratios to change based on accumulating data, in favour of the more efficacious dose arm(s), while the study is ongoing. Throughout the trial the placebo arm allocation ratio is maintained constant. These design elements can improve the statistical power in the estimation of treatment effect and increase the amount of safety and efficacy data collected for the optimal dose(s). Furthermore, these trials can provide the required evidence to potentially serve as one of two confirmatory trials needed for regulatory approval. This can reduce the need for multiple phase III trials, the total patient requirements, person-exposure risk, and ultimately the time and cost of investigational drug development programmes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.336 | 0.330 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".